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Processing of Short-Form Content in Clinical Narratives: Systematic Scoping Review.

Amila Kugic¹, Ingrid Martin², Luise Modersohn²

¹Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.

Journal of Medical Internet Research

|September 26, 2024

Summary

This summary is machine-generated.

This review examines clinical short forms in electronic health records, finding that definitions and methodologies for processing them are often unclear. Future research should address these gaps, particularly for single-character abbreviations and non-English narratives.

Keywords:

EHR clinical narratives deep learning disambiguation electronic health records human-in-the-loop, feature extraction language modeling machine learning natural language processing rule-based approach short-form expression vector representations word embedding

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Area of Science:

Natural Language Processing (NLP)
Clinical Informatics
Health Informatics

Background:

Electronic health records (EHRs) increase documentation time, leading to frequent use of abbreviations and acronyms.
This brevity in clinical narratives can impede comprehension for healthcare professionals and patients.
Understanding and processing these short forms is crucial for accurate clinical data interpretation.

Purpose of the Study:

To review the types of short forms present in clinical narratives.
To identify Natural Language Processing (NLP) techniques for short-form identification, expansion, and disambiguation.
To assess the current state of research on processing clinical short forms.

Main Methods:

Systematic scoping review adhering to PRISMA guidelines.
Searched databases including Web of Science, Embase, MEDLINE, EBMR, and ACL Anthology (Jan 2018 - Feb 2023).
Extracted short-form types and assigned NLP methodologies for identification, expansion, and disambiguation.

Main Results:

19 articles were included from 6639 records.
Rule-based methods dominated short-form identification.
String similarity and vector representations were used for expansion; embeddings and deep learning for disambiguation.

Conclusions:

Lack of explicit definitions for clinical short forms hinders reproducibility and methodology selection.
Partial adherence to NLP quality and reproducibility recommendations was observed.
Single-character abbreviations and non-English narratives are underrepresented in current research.